Abstract
ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China-Xinjiang Joint Fund under Grant U1903127 and in part by the Key Research and Development Project of Shandong Province under Grant 2018GGX101032, 2019GGX101056, 2019JZZY010706.
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Song, X., Yang, G., Wang, K. et al. Short Term ECG Classification with Residual-Concatenate Network and Metric Learning. Multimed Tools Appl 79, 22325–22336 (2020). https://doi.org/10.1007/s11042-020-09035-w
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DOI: https://doi.org/10.1007/s11042-020-09035-w